出版日期:2016-1-1
ISBN:9787564160659
作者:Edina Berlinger,Ferenc lllés,Milán Badics
书籍目录
Preface
Chapter 1: Time Series Analysis
Multivariate time series analysis
Cointegration
Vector autoregressive models
VAR implementation example
Cointegrated VAR and VECM
Volatility modeling
GARCH modeling with the rugarch package
The standard GARCH model
The Exponential GARCH model (EGARCH)
The Threshold GARCH model (TGARCH)
Simulation and forecasting
Summary
References and reading list
Chapter 2: Factor Models
Arbitrage pricing theory
Implementation of APT
Fama-French three-factor model
Modeling in R
Data selection
Estimation of APT with principal ponent analysis
Estimation of the Fama-French model
Summary
References
Chapter 3: Forecasting Volume
Motivation
The intensity of trading
The volume forecasting model
Implementation in R
The data
Loading the data
The seasonal ponent
AR(1) estimation and forecasting
SETAR estimation and forecasting
Interpreting the results
Summary
References
Chapter 4: Big Data - Advanced Analytics
Getting data from open sources
Introduction to big data analysis in R
K-means clustering on big data
Loading big matrices
Big data K-means clustering analysis
Big data linear regression analysis
Loading big data
Fitting a linear regression model on large datasets
Summary
References
Chapter 5: FX Derivatives
Terminology and notations
Currency options
Exchange options
Two-dimensional Wiener processes
The Margrabe formula
Application in R
Quanto options
Pricing formula for a call quanto
Pricing a call quanto in R
Summary
References
Chapter 6: Interest Rate Derivatives and Models
The Black model
Pricing a cap with Black's model
The Vasicek model
The Cox-Ingersoll-Ross model
Parameter estimation of interest rate models
Using the SMFI5 package
Summary
References
Chapter 7: Exotic Options
A general pricing approach
The role of dynamic hedging
How R can help a lot
A glance beyond vanillas
Greeks - the link back to the vanilla world
Pricing the Double-no-touch option
Another way to price the Double-no-touch option
The life of a Double-no-touch option - a simulation
Exotic options embedded in structurecl products
Summary
References
Chapter 8: Optimal Hedging
Hedging of derivatives
Market risk of derivatives
Static delta hedge
Dynamic delta hedge
Comparing the performance of delta hedging
Hedging in the presence of transaction costs
Optimization of the hedge
Optimal hedging in the case of absolute transaction costs
Optimal hedging in the case of relative transaction costs
Further extensions
Summary
References
Chapter 9: Fundamental Analysis
The basics of fundamental analysis
Collecting data
Revealing connections
Including multiple variables
Separating investment targets
Setting classification rules
Backtesting
Industry-specific investment
Summary
References
Chapter 10: Technical Analysis, Neural Networks, and Logoptimal Portfolios
Market efficiency
Technical analysis
The TA toolkit
Markets
Plotting charts - bitcoin
Built-in indicators
SMA and EMA
RSI
MACD
Candle patterns: key reversal
Evaluating the signals and managing the position
A word on money management
Wraping up
Neural works,
Forecasting bitcoin prices
Evaluation of the strategy
Logoptimal portfolios
A universally consistent, non-parametric investment strategy
Evaluation of the strategy
Summary
References
Chapter 11: Asset and Liability Management
Data preparation
Data source at first glance
Cash-flow generator functions
Preparing the cash-flow
Interest rate risk measurement
Liquidity risk measurement
Modeling non-maturity deposits
A Model of deposit interest rate development
Static replication of non-maturity deposits
Summary
References
Chapter 12: Capital Adequacy
Principles of the Basel Accords
Basel I
Basel II
Minimum capital requirements
Supervisory review
Transparency
Basel III
Risk measures
Analytical VaR
Historical VaR
Monte-Carlo simulation
Risk categories
Market risk
Credit risk
Operational risk
Summary
References
Chapter 13: Systemic Risks
Systemic risk in a nutshell
The dataset used in our examples
Core-periphery deposition
Implementation in R
Results
The Simulation method
The simulation
Implementation in R
Results
Possible interpretations and suggestions
Summary
References
Index
作者简介
Edina Berlinger、Ferenc lllés、Milán Badics等*的《精通R语言--用于量化金融(影印版)( 英文版)》是关于如何运用R语言的实践指南，按循序渐进的步骤编写而成。从时间序列分析开始逐步介绍，你还将从中学到如何预测VWAP交易规模。本书涵盖了FX衍生品、利率衍生品及*优对冲等其他相关主题。*后几章将讲述流动性风险管理、风险评估等*多内容。
本书立足实际，介绍了量化金融概念和R语言建模方法，让你可以自行建立定制化的交易系统。读完本书后，你将可以熟练运用R语言实现各种金融技术并且能够做出正确的金融决策。
该书旨在为那些需要学习使用R语言进行高级建模的量化金融领域人士而准备。如果你希望完美地跟上每个章节的节奏，需要在量化金融方面具备中级水平，并且需要准备R语言相关基础知识。